Professor of Management
Tomohiro Ando joined Melbourne Business School in 2015 and is currently a Professor of Management.
He received his PhD from Kyushu University in Japan, and taught at University of California Berkeley, University of California Los Angeles, and Keio University. He held a visiting scholar position at the University of Chicago Booth School of Business.
Tomohiro's recent academic activities, consulting, engagement, joint research with business/public organisations include topics related to analytics, marketing, strategic management and more. Tomohiro is the director of “Centre of Excellence for Big Data, AI and Analytics” that conducts research related to big data, AI and analytics. Centre’s main research topics are connected but not limited to accounting, economics, human resource management, finance, health care, marketing, operations management, public policy, strategy, and sustainability. Tomohiro’s research has appeared in Annals of Statistics, Journal of the American Statistical Association, Journal of Econometrics, Management Science, and other distinguished publications.
Tomohiro is Co-director of “Doctoral Program in Business Administration and Analytics”. He teaches Analytics for Strategic Management, Consulting and Data Analysis in Senior Executive MBA, Executive MBA, part-time MBA and Master of Business Analytics programs. He also teaches at Advanced Management Program, designed for senior executives. He is co-organiser of Virtual Discussion Seminar “Frontiers of Big Data, AI and Analytics". This event series aims to unleash ideas and insights for harnessing the successful future of business & society.
‘Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks’, Ando, T. Greenwood-Nimmo, M. & Shin, Y. 2021, Management Science.
‘Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity’, Ando, T. Bai, J. & Li, K. 2021, Journal of Econometrics.
‘Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity’, Ando, T. & Bai, J, 2020, Journal of the American Statistical Association.
‘A weight-relaxed model averaging approach for high-dimensional generalized linear models’, Ando, T. & Li, K-C, 2018, Annals of Statistics.
‘Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures’, Ando, T. & Bai, J, 2018, Journal of the American Statistical Association.
‘A model averaging approach for high-dimensional regression’, Ando, T & Li, K-C, 2014, Journal of the American Statistical Association.
‘A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model’, Zellner, A. & Ando, T, 2010, Journal of Econometrics.
‘Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models’, Ando, T. 2007, Biometrika.
‘Bayesian information criteria and smoothing parameter selection in radial basis function networks’, Konishi, S. Ando, T. & Imoto, S. 2004, Biometrika.